Browsing by Author "Maia, Nuno M. M."
Now showing 1 - 5 of 5
Results Per Page
Sort Options
- Damage detection and quantification using transmissibilityPublication . Maia, Nuno M. M.; Almeida, Raquel A. B.; Urgueira, Antonio P. V.; Sampaio, Rui Pedro ChedasStructures experience various types of loads along their lifetime, which can be either static or dynamic and may be associated to phenomena of corrosion and chemical attack, among others. As a consequence, different types of structural damage can be produced; the deteriorated structure may have its capacity affected, leading to excessive vibration problems or even possible failure. It is very important to develop methods that are able to simultaneously detect the existence of damage and to quantify its extent. In this paper the authors propose a method to detect and quantify structural damage, using response transmissibilities measured along the structure. Some numerical simulations are presented and a comparison is made with results using frequency response functions. Experimental tests are also undertaken to validate the proposed technique. (C) 2011 Elsevier Ltd. All rights reserved.
- Elastically restrained Bernoulli-Euler beams applied to rotary machinery modellingPublication . Silva, Tiago A. N.; Maia, Nuno M. M.Facing the lateral vibration problem of a machine rotor as a beam on elastic supports in bending, the authors deal with the free vibration of elastically restrained Bernoulli-Euler beams carrying a finite number of concentrated elements along their length. Based on Rayleigh's quotient, an iterative strategy is developed to find the approximated torsional stiffness coefficients, which allows the reconciliation between the theoretical model results and the experimental ones, obtained through impact tests. The mentioned algorithm treats the vibration of continuous beams under a determined set of boundary and continuity conditions, including different torsional stiffness coefficients and the effect of attached concentrated masses and rotational inertias, not only in the energetic terms of the Rayleigh's quotient but also on the mode shapes, considering the shape functions defined in branches. Several loading cases are examined and examples are given to illustrate the validity of the model and accuracy of the obtained natural frequencies.
- Estimation of rotational frequency response functionsPublication . Silva, Tiago Alexandre Narciso; Maia, Nuno M. M.As it is widely known, in structural dynamic applications, ranging from structural coupling to model updating, the incompatibility between measured and simulated data is inevitable, due to the problem of coordinate incompleteness. Usually, the experimental data from conventional vibration testing is collected at a few translational degrees of freedom (DOF) due to applied forces, using hammer or shaker exciters, over a limited frequency range. Hence, one can only measure a portion of the receptance matrix, few columns, related to the forced DOFs, and rows, related to the measured DOFs. In contrast, by finite element modeling, one can obtain a full data set, both in terms of DOFs and identified modes. Over the years, several model reduction techniques have been proposed, as well as data expansion ones. However, the latter are significantly fewer and the demand for efficient techniques is still an issue. In this work, one proposes a technique for expanding measured frequency response functions (FRF) over the entire set of DOFs. This technique is based upon a modified Kidder's method and the principle of reciprocity, and it avoids the need for modal identification, as it uses the measured FRFs directly. In order to illustrate the performance of the proposed technique, a set of simulated experimental translational FRFs is taken as reference to estimate rotational FRFs, including those that are due to applied moments.
- A global optimization approach based on adaptive populationsPublication . Silva, Tiago A. N.; Loja, Amélia; Carvalho, Alda; Maia, Nuno M. M.; Barbosa, JoaquimThe solution of inverse problems based on experimental data is itself an important research issue. In this context and assuming that an experimental sample is available, rather than trying to find a specific deterministic solution for the inverse problem, one aims to determine the probabilistic distribution of the modelling parameters, based on the minimization of the dissimilarity between the empirical cumulative distribution function of an experimental solution and its simulation counterpart. The present paper presents na innovative framework, where Differential Evolution is extended in order to estimate not only an optimal set of modelling parameters, but to estimate their optimal probabilistic distributions. Additionally, the Adaptive Empirical Distributions optimization scheme is here introduced. Both schemes rely on the two samples Kolmogorov-Smirnov goodness-offit test in order to evaluate the resemblance between two empirical cumulative distribution functions. A numerical example is considered in order to assess the performance of the proposed strategies and validity of their solutions.
- Parameter selection and covariance updatingPublication . Silva, Tiago N. A.; Maia, Nuno M. M.; Link, Michael; Mottershead, John E.A simple expression is developed for covariance-matrix correction in stochastic model updating. The need for expensive forward propagation of uncertainty through the model is obviated by application of a formula based only on the sensitivity of the outputs at the end of a deterministic updating process carried out on the means of the parameters. Two previously published techniques are show to reduce to the same simple formula within the assumption of small perturbation about the mean. It is shown, using a simple numerical example, that deterministic updating of the parameter means can result in correct reconstruction of the output means even when the updating parameters are wrongly chosen. If the parameters are correctly chosen, then the covariance matrix of the outputs is correctly reconstructed, but when the parameters are wrongly chosen is found that the output covariance is generally not reconstructed accurately. Therefore, the selection of updating parameters on the basis of reconstructing the output means is not sufficient to ensure that the output covariances will be well reconstructed. Further theory is then developed by assessing the contribution of each candidate parameter to the output covariance matrix, thereby enabling the selection of updating parameters to ensure that both the output means and covariances are reconstructed by the updated model.